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首页> 外文会议>International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing >Identifying New Methylated Arginines via Granular Decision Fusion with SVM Modeling
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Identifying New Methylated Arginines via Granular Decision Fusion with SVM Modeling

机译：用SVM造型通过粒状决策融合鉴定新的甲基化精氨酸

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摘要

Traditional methods of discovering new methylated arginines in proteins involve conducting delicate experiments to examine every arginine in the primary sequence. Such process is labor-intensive and time-consuming. To speed up this process, one popular way is using machine learning method to model the underlying mechanism of protein methylation based on known methylated proteins and then suggesting candidate positions on unknown proteins. In this paper, we first collect several proteins methylated by different families of PRMTs, and then use granular computing methods to build a granular decision fusion method based on SVM modeling. Such decision fusion method can produce high prediction accuracy. More importantly, we use this method to successfully discover several highly possible methylation sites on some unknown proteins, biological experiments have verified our results.
机译：在蛋白质中发现新的甲基化精氨酸的传统方法涉及进行细腻的实验，以在主要序列中检查每种精氨酸。这种过程是劳动密集型和耗时的。为了加快这个过程，一种流行的方式是使用机器学习方法基于已知的甲基化蛋白来模拟蛋白质甲基化的底层机制，然后表明未知蛋白质上的候选位置。在本文中，我们首先收集几种甲基化的PRMTS甲基化，然后使用颗粒计算方法来构建基于SVM造型的粒度决策融合方法。这种决策融合方法可以产生高预测精度。更重要的是，我们使用这种方法在一些未知的蛋白质上成功发现几个高度可能的甲基化位点，生物实验已经验证了我们的结果。

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